TY - JOUR
T1 - The use of mobile phone data to inform analysis of COVID-19 pandemic epidemiology
AU - Grantz, Kyra H.
AU - Meredith, Hannah R.
AU - Cummings, Derek A.T.
AU - Metcalf, C. Jessica E.
AU - Grenfell, Bryan T.
AU - Giles, John R.
AU - Mehta, Shruti
AU - Solomon, Sunil
AU - Labrique, Alain
AU - Kishore, Nishant
AU - Buckee, Caroline O.
AU - Wesolowski, Amy
N1 - Publisher Copyright:
© 2020, The Author(s).
PY - 2020/12/1
Y1 - 2020/12/1
N2 - The ongoing coronavirus disease 2019 (COVID-19) pandemic has heightened discussion of the use of mobile phone data in outbreak response. Mobile phone data have been proposed to monitor effectiveness of non-pharmaceutical interventions, to assess potential drivers of spatiotemporal spread, and to support contact tracing efforts. While these data may be an important part of COVID-19 response, their use must be considered alongside a careful understanding of the behaviors and populations they capture. Here, we review the different applications for mobile phone data in guiding and evaluating COVID-19 response, the relevance of these applications for infectious disease transmission and control, and potential sources and implications of selection bias in mobile phone data. We also discuss best practices and potential pitfalls for directly integrating the collection, analysis, and interpretation of these data into public health decision making.
AB - The ongoing coronavirus disease 2019 (COVID-19) pandemic has heightened discussion of the use of mobile phone data in outbreak response. Mobile phone data have been proposed to monitor effectiveness of non-pharmaceutical interventions, to assess potential drivers of spatiotemporal spread, and to support contact tracing efforts. While these data may be an important part of COVID-19 response, their use must be considered alongside a careful understanding of the behaviors and populations they capture. Here, we review the different applications for mobile phone data in guiding and evaluating COVID-19 response, the relevance of these applications for infectious disease transmission and control, and potential sources and implications of selection bias in mobile phone data. We also discuss best practices and potential pitfalls for directly integrating the collection, analysis, and interpretation of these data into public health decision making.
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U2 - 10.1038/s41467-020-18190-5
DO - 10.1038/s41467-020-18190-5
M3 - Article
C2 - 32999287
AN - SCOPUS:85091717740
SN - 2041-1723
VL - 11
JO - Nature communications
JF - Nature communications
IS - 1
M1 - 4961
ER -